{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:25:50Z","timestamp":1750220750717,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T00:00:00Z","timestamp":1587686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,4,24]]},"DOI":"10.1145\/3398329.3398349","type":"proceedings-article","created":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T04:43:15Z","timestamp":1590986595000},"page":"197-205","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["FuzzyNet"],"prefix":"10.1145","author":[{"given":"Ariyo","family":"Oluwasanmi","sequence":"first","affiliation":[{"name":"Software Engineering, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Ebere","family":"Eziefuna","sequence":"additional","affiliation":[{"name":"Biomedical Engineering University of Electronic Science and Technology of China Chengdu, China"}]},{"given":"Favour","family":"Ekong","sequence":"additional","affiliation":[{"name":"Software Engineering, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Akeem","family":"Shokanbi","sequence":"additional","affiliation":[{"name":"Computer Science, Southwest Jiaotong University, Chengdu, China"}]},{"given":"Edward","family":"Baagyere","sequence":"additional","affiliation":[{"name":"Software Engineering, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Zhiguang","family":"Qin","sequence":"additional","affiliation":[{"name":"Software Engineering, University of Electronic Science and Technology of China, Chengdu, China"}]}],"member":"320","published-online":{"date-parts":[[2020,5,31]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"1","article-title":"Recent progress in semantic image segmentation","author":"Liu X.","year":"2018","unstructured":"X. Liu , Z. Deng , and Y. Yang , \" Recent progress in semantic image segmentation ,\" Artificial Intelligence Review , pp. 1 -- 18 , 2018 . X. Liu, Z. Deng, and Y. Yang, \"Recent progress in semantic image segmentation,\" Artificial Intelligence Review, pp.1--18, 2018.","journal-title":"Artificial Intelligence Review"},{"key":"e_1_3_2_1_2_1","unstructured":"L. Perez and J. Wang \"The effectiveness of data augmentation in image classification using deep learning \" 2017 [Online]. Available: https:\/\/arxiv.org\/abs\/1712.04621 L. Perez and J. Wang \"The effectiveness of data augmentation in image classification using deep learning \" 2017 [Online]. Available: https:\/\/arxiv.org\/abs\/1712.04621"},{"key":"e_1_3_2_1_3_1","unstructured":"L. Fan W. Wang F. Zha J. Yan \"Exploring New Backbone and Attention Module for Semantic Segmentation in Street Scenes \" IEEE Access PP. 1--1. 10.1109\/2880877 2018.  L. Fan W. Wang F. Zha J. Yan \"Exploring New Backbone and Attention Module for Semantic Segmentation in Street Scenes \" IEEE Access PP. 1--1. 10.1109\/2880877 2018."},{"key":"e_1_3_2_1_4_1","first-page":"1520","volume-title":"Learning deconvolution network for semantic segmentation,\" 2015 IEEE International Conference on Computer Vision (ICCV)","author":"Noh H.","year":"2015","unstructured":"H. Noh , S. Hong , and B. Han , \" Learning deconvolution network for semantic segmentation,\" 2015 IEEE International Conference on Computer Vision (ICCV) , pp. 1520 -- 1528 , 2015 . H. Noh, S. Hong, and B. Han, \"Learning deconvolution network for semantic segmentation,\" 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1520--1528, 2015."},{"key":"e_1_3_2_1_5_1","first-page":"1175","volume-title":"The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","author":"Jegou S.","year":"2017","unstructured":"S. Jegou , M. Drozdzal , D. Vazquez , A. Romero , and Y. Bengio , \" The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , pp. 1175 -- 1183 , 2017 . S. Jegou, M. Drozdzal, D. Vazquez, A. Romero, and Y. Bengio, \"The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1175--1183, 2017."},{"key":"e_1_3_2_1_6_1","volume-title":"Edge gradient feature and long distance dependency for image semantic segmentation,\" In IET Computer Vision","author":"Zhou H.","year":"2018","unstructured":"H. Zhou , A. Han , H. Yang and J. Zhang , \" Edge gradient feature and long distance dependency for image semantic segmentation,\" In IET Computer Vision , 2018 . H. Zhou, A. Han, H. Yang and J. Zhang, \"Edge gradient feature and long distance dependency for image semantic segmentation,\" In IET Computer Vision, 2018."},{"key":"e_1_3_2_1_7_1","volume-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation,\" In ECCV","author":"Chen L. C.","year":"2018","unstructured":"L. C. Chen , Y. Zhu , G. Papandreou , F. Schroff , and H. Adam , \" Encoder-decoder with atrous separable convolution for semantic image segmentation,\" In ECCV , 2018 . L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, \"Encoder-decoder with atrous separable convolution for semantic image segmentation,\" In ECCV, 2018."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2868801"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2018.5218"},{"key":"e_1_3_2_1_10_1","first-page":"3431","volume-title":"Fully convolutional networks for semantic segmentation,\" 2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR)","author":"Long J.","year":"2015","unstructured":"J. Long , E. Shelhamer , and T. Darrell , \" Fully convolutional networks for semantic segmentation,\" 2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR) , pp. 3431 -- 3440 , 2015 J. Long, E. Shelhamer, and T. Darrell, \"Fully convolutional networks for semantic segmentation,\" 2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 3431--3440, 2015"},{"key":"e_1_3_2_1_11_1","volume-title":"U-net: Convolutional networks for biomedical image segmentation,\" In MICCAI","author":"Ronneberger O.","year":"2015","unstructured":"O. Ronneberger , P. Fischer , and T. Brox , \" U-net: Convolutional networks for biomedical image segmentation,\" In MICCAI , 2015 . O. Ronneberger, P. Fischer, and T. Brox, \"U-net: Convolutional networks for biomedical image segmentation,\" In MICCAI, 2015."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"e_1_3_2_1_13_1","volume-title":"Rethinking atrous convolution for semantic image segmentation,\" CoRR, abs\/1706.05587","author":"Chen L. C.","year":"2017","unstructured":"L. C. Chen , G. Papandreou , F. Schroff , and H. Adam , \" Rethinking atrous convolution for semantic image segmentation,\" CoRR, abs\/1706.05587 , 2017 . L. C. Chen, G. Papandreou, F. Schroff, and H. Adam, \"Rethinking atrous convolution for semantic image segmentation,\" CoRR, abs\/1706.05587, 2017."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","first-page":"6230","DOI":"10.1109\/CVPR.2017.660","volume-title":"Pyramid scene parsing network,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Zhao H.","year":"2017","unstructured":"H. Zhao , J. Shi , X. Qi , X. Wang , and J. Jia , \" Pyramid scene parsing network,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 6230 -- 6239 , 2017 . H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, \"Pyramid scene parsing network,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6230--6239, 2017."},{"key":"e_1_3_2_1_15_1","first-page":"5168","volume-title":"Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Lin G.","year":"2017","unstructured":"G. Lin , A. Milan , C. Shen , and I. D. Reid , \" Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 5168 -- 5177 , 2017 . G. Lin, A. Milan, C. Shen, and I. D. Reid, \"Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5168--5177, 2017."},{"key":"e_1_3_2_1_16_1","first-page":"1743","volume-title":"Large kernel matters improve semantic segmentation by global convolutional network,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Peng C.","year":"2017","unstructured":"C. Peng , X. Zhang , G. Yu , G. Luo , and J. Sun , \" Large kernel matters improve semantic segmentation by global convolutional network,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 1743 -- 1751 , 2017 C. Peng, X. Zhang, G. Yu, G. Luo, and J. Sun, \"Large kernel matters improve semantic segmentation by global convolutional network,\" 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1743--1751, 2017"},{"key":"e_1_3_2_1_17_1","unstructured":"L. C. Chen G. Papandreou I. Kokkinos K. Murphy and A. L Yuille \"Semantic image segmentation with deep convolutional nets and fully connected crfs \" In International Conference of Learning Representations (ICLR) 2015.  L. C. Chen G. Papandreou I. Kokkinos K. Murphy and A. L Yuille \"Semantic image segmentation with deep convolutional nets and fully connected crfs \" In International Conference of Learning Representations (ICLR) 2015."},{"key":"e_1_3_2_1_18_1","volume-title":"Parsenet: Looking wider to see better,\" CoRR, abs\/1506.04579","author":"Liu W.","year":"2015","unstructured":"W. Liu , A. Rabinovich , and A. C. Berg , \" Parsenet: Looking wider to see better,\" CoRR, abs\/1506.04579 , 2015 . W. Liu, A. Rabinovich, and A. C. Berg, \"Parsenet: Looking wider to see better,\" CoRR, abs\/1506.04579, 2015."},{"key":"e_1_3_2_1_19_1","first-page":"3684","volume-title":"DenseASPP for semantic segmentation in street scenes,\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Yang M.","year":"2018","unstructured":"M. Yang , K. Yu , C. Zhang , Z. Li , and K. Yang , \" DenseASPP for semantic segmentation in street scenes,\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition , pp. 3684 -- 3692 , 2018 M. Yang, K. Yu, C. Zhang, Z. Li, and K. Yang, \"DenseASPP for semantic segmentation in street scenes,\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3684--3692, 2018"},{"key":"e_1_3_2_1_20_1","volume-title":"Enet: A deep neural network architecture for real-time semantic segmentation,\" CoRR, abs\/1606.02147","author":"Paszke A.","year":"2016","unstructured":"A. Paszke , A. Chaurasia , S. Kim , and E. Culurciello , \" Enet: A deep neural network architecture for real-time semantic segmentation,\" CoRR, abs\/1606.02147 , 2016 . A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello, \"Enet: A deep neural network architecture for real-time semantic segmentation,\" CoRR, abs\/1606.02147, 2016."},{"key":"e_1_3_2_1_21_1","volume-title":"Icnet for real-time semantic segmentation on high-resolution images,\" In ECCV","author":"Zhao H.","year":"2018","unstructured":"H. Zhao , X. Qi , X. Shen , J. Shi , and J. Jia , \" Icnet for real-time semantic segmentation on high-resolution images,\" In ECCV , 2018 . H. Zhao, X. Qi, X. Shen, J. Shi, and J. Jia, \"Icnet for real-time semantic segmentation on high-resolution images,\" In ECCV, 2018."},{"key":"e_1_3_2_1_22_1","first-page":"770","volume-title":"Deep residual learning for image recognition,\" 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He K.","year":"2016","unstructured":"K. He , X. Zhang , S. Ren , and J. Sun , \" Deep residual learning for image recognition,\" 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 770 -- 778 , 2016 K. He, X. Zhang, S. Ren, and J. Sun, \"Deep residual learning for image recognition,\" 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770--778, 2016"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2005.10.001"},{"issue":"2","key":"e_1_3_2_1_25_1","first-page":"338","article-title":"The Pascal visual object classes (VOC) challenge","volume":"88","author":"Everingham M.","year":"2009","unstructured":"M. Everingham , L. Van Gool , C. K. I. Williams , J. Winn , and A. Zisserman , \" The Pascal visual object classes (VOC) challenge ,\" Int. J. Comput. Vis. , vol. 88 , no. 2 , pp. 303 338 , Sep. 2009 . M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, \"The Pascal visual object classes (VOC) challenge,\" Int. J. Comput. Vis., vol. 88, no. 2, pp. 303 338, Sep. 2009.","journal-title":"Int. J. Comput. Vis."},{"key":"e_1_3_2_1_26_1","first-page":"44","volume-title":"Eur. Conf. Comput. Vis. (ECCV)","author":"Brostow G. J.","year":"2008","unstructured":"G. J. Brostow , J. Shotton , J. Fauqueur , and R. Cipolla , \" Segmentation and recognition using structure from motion point clouds,\" in Proc . Eur. Conf. Comput. Vis. (ECCV) , Marseille, France , Oct. 2008 , pp. 44 57. G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla, \"Segmentation and recognition using structure from motion point clouds,\" in Proc. Eur. Conf. Comput. Vis. (ECCV), Marseille, France, Oct. 2008, pp. 44 57."},{"key":"e_1_3_2_1_27_1","first-page":"1","volume-title":"Change detection.net A new change detection benchmark dataset,\" In IEEE CVPR Workshops","author":"Goyette N.","year":"2012","unstructured":"N. Goyette , P. M. Jodoin , F. Porikli , J. Konrad , and P. Ishwar , \" Change detection.net A new change detection benchmark dataset,\" In IEEE CVPR Workshops , pages 1 -- 8 ., 2012 N. Goyette, P. M. Jodoin, F. Porikli, J. Konrad, and P. Ishwar, \"Change detection.net A new change detection benchmark dataset,\" In IEEE CVPR Workshops, pages 1--8., 2012"},{"key":"e_1_3_2_1_28_1","first-page":"1","volume-title":"ApesNet: a pixel-wise efficient segmentation network for embedded devices,\" In 14th ACM\/IEEE Symposium on Embedded Systems For Real-time Multimedia","author":"Wu C.","year":"2016","unstructured":"C. Wu , H. Cheng , S. Li , H. Li and Y. Chen , \" ApesNet: a pixel-wise efficient segmentation network for embedded devices,\" In 14th ACM\/IEEE Symposium on Embedded Systems For Real-time Multimedia , pages 1 -- 7 , 2016 C. Wu, H. Cheng, S. Li, H. Li and Y. Chen, \"ApesNet: a pixel-wise efficient segmentation network for embedded devices,\" In 14th ACM\/IEEE Symposium on Embedded Systems For Real-time Multimedia, pages 1--7, 2016"},{"key":"e_1_3_2_1_29_1","first-page":"1","article-title":"LinkNet: Exploiting encoder representations for efficient semantic segmentation","author":"Chaurasia A.","year":"2017","unstructured":"A. Chaurasia and E. Culurciello , \" LinkNet: Exploiting encoder representations for efficient semantic segmentation ,\" IEEE Vis. Commun. Image Process. , pp. 1 -- 4 . Dec. 2017 . A. Chaurasia and E. Culurciello, \"LinkNet: Exploiting encoder representations for efficient semantic segmentation,\" IEEE Vis. Commun. Image Process., pp. 1--4. Dec. 2017.","journal-title":"IEEE Vis. Commun. Image Process."},{"key":"e_1_3_2_1_30_1","unstructured":"F. Yu and V. Koltun \"Multi-scale context aggregation by dilated convolutions \"[Online].Available:https:\/\/arxiv.org\/abs\/1511.071222.2015  F. Yu and V. Koltun \"Multi-scale context aggregation by dilated convolutions \"[Online].Available:https:\/\/arxiv.org\/abs\/1511.071222.2015"},{"key":"e_1_3_2_1_31_1","volume-title":"Yu and Nong Sang, \"BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation,\" European Conference on Computer Vision","author":"Yu C.","year":"2018","unstructured":"C. Yu , J. Wang , G. Peng , C. Gao , G. Yu and Nong Sang, \"BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation,\" European Conference on Computer Vision ., 2018 C. Yu, J. Wang, G. Peng, C. Gao, G. Yu and Nong Sang, \"BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation,\" European Conference on Computer Vision., 2018"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2814568"},{"key":"e_1_3_2_1_33_1","first-page":"3376","volume-title":"IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Shakhnarovich M.","year":"2015","unstructured":"M. Mostajabi P. Yadollahpour G. Shakhnarovich , \"Feedforward semantic segmentation with zoom-out features,\" Proc . IEEE Conf. Comput. Vis. Pattern Recognit. pp. 3376 -- 3385 2015 . M. Mostajabi P. Yadollahpour G. Shakhnarovich, \"Feedforward semantic segmentation with zoom-out features,\" Proc. IEEE Conf. Comput. Vis. Pattern Recognit. pp. 3376--3385 2015."},{"key":"e_1_3_2_1_34_1","first-page":"3224","volume-title":"Gaussian conditional random field network for semantic segmentation,\" In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Vemulapalli R.","year":"2016","unstructured":"R. Vemulapalli , O. Tuzel , M.-Y. Liu , and R. Chellapa , \" Gaussian conditional random field network for semantic segmentation,\" In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 3224 -- 3233 , 2016 . R. Vemulapalli, O. Tuzel, M.-Y. Liu, and R. Chellapa, \"Gaussian conditional random field network for semantic segmentation,\" In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3224--3233, 2016."},{"key":"e_1_3_2_1_35_1","first-page":"1377","volume-title":"Semantic image segmentation via deep parsing network,\" In Proceedings of the IEEE International Conference on Computer Vision","author":"Liu Z.","year":"2015","unstructured":"Z. Liu , X. Li , P. Luo , C.-C. Loy , and X. Tang , \" Semantic image segmentation via deep parsing network,\" In Proceedings of the IEEE International Conference on Computer Vision , pages 1377 -- 1385 , 2015 . Z. Liu, X. Li, P. Luo, C.-C. Loy, and X. Tang, \"Semantic image segmentation via deep parsing network,\" In Proceedings of the IEEE International Conference on Computer Vision, pages 1377--1385, 2015."},{"key":"e_1_3_2_1_36_1","first-page":"3194","volume-title":"Efficient piecewise training of deep structured models for semantic segmentation,\" In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Lin G.","year":"2016","unstructured":"G. Lin , C. Shen , A. van den Hengel , and I. Reid , \" Efficient piecewise training of deep structured models for semantic segmentation,\" In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 3194 -- 3203 , 2016 . G. Lin, C. Shen, A. van den Hengel, and I. Reid, \"Efficient piecewise training of deep structured models for semantic segmentation,\" In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3194--3203, 2016."}],"event":{"name":"CNIOT2020: 2020 International Conference on Computing, Networks and Internet of Things","sponsor":["University of Salamanca University of Salamanca","The University of Adelaide, Australia","Edinburgh Napier University, UK Edinburgh Napier University, UK","University of Sydney Australia"],"location":"Sanya China","acronym":"CNIOT2020"},"container-title":["Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3398329.3398349","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3398329.3398349","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:53Z","timestamp":1750199933000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3398329.3398349"}},"subtitle":["Context Encoding and Spatial Fuzzy Refinement Network in Semantic Segmentation"],"short-title":[],"issued":{"date-parts":[[2020,4,24]]},"references-count":36,"alternative-id":["10.1145\/3398329.3398349","10.1145\/3398329"],"URL":"https:\/\/doi.org\/10.1145\/3398329.3398349","relation":{},"subject":[],"published":{"date-parts":[[2020,4,24]]},"assertion":[{"value":"2020-05-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}